Based on the sub-cluster type and geometric proximity a score between 0 and 1 isassigned between any two sub-clusters starting with the cluster in consideration

The higher the score the higher the probability of a link

A cut-off threshold is obtained for an energy by tuning with events

Energy dependence

6

Performance at LOI

Study just how much is contributed because of leakage

Leakage study at 500GeV

and 1TeV

Produce data sets aSiD02-like detectorMCwith 6

HCAL

for 1TeV, 500GeV, 200GeV

•

Change Steel for Cu for absorber

•

Increase to 54 layers from 40 layers in HCAL

•

1.7

more material in HCAL

•

No gap between HCAL andMuon

endcap

(instead of 10 cm)

Compare sid02 with sid02-Cu at various energies

Check leakage by observing # hits inMuon

detector : punch thru; a measure of leakage

Simultaneously

study the corresponding change in Energy resolution

The relative measure from the two gives an approximate semi-quantitative measure

of leakagevs

performance

7

Although substantial leakage is present at 500GeV

confusion is clearly important

Punch-throughmuon

hits

SiD02-Cu

SiD02

8

Resolution study(SiD02-Cu comparison)

real tracking

SiD02-Cu

SiD02

9

Conclusions from Leakage study

10

Although substantial leakage is present at 500GeV, algorithm

(confusion) has an important part

•

At 1TeV

leakage comparison shows large difference in performance

betweenSiD-nominal (dashed) andSiD-Cu detectors (solid)

•

At 500GeV

leakage comparison shows significant difference in performance

betweenSiD-nominal (dashed) andSiD-Cu detectors (solid)

•

Performance of 1TeV

SiD-Cu is similar to 500GeV

SiD-nominal in leakage

•

At 1TeV

performance in resolution is worse with

SiD-nominal (dashed) andSiD-Cu detectors (solid)

•

At 500GeV

performance in resolution is worse with

SiD-nominal (dashed) andSiD-Cu detectors (solid)

•

However : The difference of performance in resolution between 1TeV

SiD-Cu and 500GeV

SiD-nominal is not similar to that in leakage

A 500GeV

qqbar

event from one side jet

Raw

MC

hits

are

displayed,

each

color

shows

an

individual

shower

Contains a low energy 12GeV

neutralhadron

andseveral photons in the ECAL;charged hadrons interacts

reconstructed

The same as before shown without theisolated and unmatched hits : still no PFAreconstruction, only with knowledge of MC

Now shown without the isolated hits butafter reconstruction,alogorithm

of chargedhadron

track-cluster match (cone algorithm)

p (orange) = 119GeV, E/p match, enough hits (green) = 17GeV

, algorithm

introduced a cone-like path in thereclustering

to pick up secondary neutrals; butended up being too aggressive in stealing pieces from the lowmomenta

tracks

has a low energy 12GeV

neutralhadron

and several photons present in theECAL; interaction of chargedhadron

RefinedCheatCluster

RefinedCluster

-

sharedhits

p (orange) = 119GeV,E/p match, enough

hits (green) = 17GeV

reconstructed

13

Had introduced a cone-like path in thereclustering

to pick upsecondary neutrals; but ends up being too aggressive

Diagnosis of `A’problem: an example

DCA

IP

DirAngle

PosAngle

Seed

Cluster

Interaction point

The `Cone’ Algorithm

A detailed Study

All plots show variables defined for links between a seed and a cluster. If theseed and the cluster belong to the same truth particle, the link is quoted as“Signal” otherwise it is quoted as “Background”

Top-Left Plot:

Scores just before the First cone algorithm runs.

Top-Middle Plot:

Scores just after the First cone algorithm runs.

Top-Right Plot:

Impact Parameter (IP): Distance between the center of theseed and the straight line from the center of the cluster extrapolated along thecluster’s direction

Bottom-Left Plot:

Distance of closest approach (DCA) between two straightlines taken respectively from the center of the seed and the center of the clusterand along the respective directions.

Bottom-Middle Plot:

Angle at the interaction point formed by the positionsof the seed and the cluster.

Bottom-Right Plot:

Angular difference between the direction of the seedand the direction of the cluster.

Left plot:

Scores before the first cone algorithm.

Right plot:

Scores after the cone algorithm.

While Signal/Background discrimination is better after the first conealgorithm, backgrounds now peak in the Signal region.

Scoredisteribution

for links when the first cone algorithm modifies

the score.

Correlated Variables

now zoom on signal region: look at links when the first cone algorithmgives a high score (>0.8).

Sharing of hits:

Breaking up into smaller clusters

Extending to smaller pieces

Next Steps

Allow flexibility in assignment of hits in clusters from tracks in the vicinity;

Allocate after arbitration

Check where exactly the `cone’ is needed, modify this, dump the rest

Wait for results from ongoing study here….

Faster turn around time

Improved resolution

Next major step : Incorporate the PFA with realisticSiD

(SiD03) geometry

Now progressing in parallel

Expect to take a step backward: non-trivial

Improve sophisticated modifications for special types of clusters, like backscattering,complex rare occurrences